Dify vs Flowise vs LangFlow: Open Source AI Workflow Builders 2026

The Rise of No-Code AI Workflow Builders

Building AI applications used to require writing LangChain or LlamaIndex code directly. Now, visual workflow builders let you connect AI nodes, data sources, and tools through drag-and-drop interfaces — dramatically lowering the barrier to building AI-powered applications.

Three tools dominate the open source space: Dify as the all-in-one platform, Flowise as the developer-friendly visual builder, and LangFlow as the closest to a visual LangChain experience.

Which one is right for your team depends on how much you value ease of use vs. control, and whether you need a complete platform vs. a focused workflow tool.

TL;DR

• Dify (80K+ stars): Best all-in-one platform. RAG + workflows + API + monitoring in one tool. Best for teams building production AI applications.
• Flowise (35K+ stars): Best for rapid prototyping and developers. Acquired by Workday (2025). Excellent for complex agent workflows.
• LangFlow (45K+ stars): Best for LangChain users and teams who want visual workflow building with full LangChain component access.

Quick Comparison

Feature	Dify	Flowise	LangFlow
GitHub Stars	80K+	35K+	45K+
Target User	Dev + non-dev	Developer	Developer
RAG built-in	Yes (excellent)	Yes	Yes
Agent support	Yes	Yes	Yes
Visual workflow	Yes	Yes	Yes
API generation	Yes (automatic)	Yes	Yes
Monitoring/analytics	Yes	Limited	Limited
Model management	Yes	Via config	Via config
Self-hosting ease	Medium	Easy	Easy
License	MIT	Apache 2.0	MIT

Dify — Best All-in-One Platform

Dify is the most comprehensive option. It's not just a workflow builder — it's a complete platform for building, deploying, and monitoring AI applications, with RAG knowledge bases, model management, and API endpoints all included.

What Makes It Stand Out

Integrated knowledge bases: Create vector knowledge bases directly in Dify. Upload documents, configure chunking and embedding, and use them as retrieval sources in your workflows — all through the same interface. No external vector database configuration required for standard setups.

Workflow builder: Visual canvas for building AI workflows. Connect LLM nodes, tool nodes, conditional branching, loops, HTTP requests, and code execution. Variables flow between nodes.

Application types:

• Chatbot: Conversational interface with memory
• Completion: Single-turn text generation
• Workflow: Complex multi-step automation
• Agent: Tool-using AI agent

Automatic API: Every Dify application automatically gets an API endpoint. Deploy a chatbot, get an API key, start calling it from your application.

Monitoring dashboard: Usage analytics, token consumption, latency by node, error rates, and conversation logs. This visibility is what makes Dify production-ready vs. tools that are primarily prototyping environments.

Model management: Configure multiple LLM providers in one place. Switch models without changing workflow code.

Self-Hosting

git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
docker compose up -d

Dify runs as multiple containers (main app, worker, web, PostgreSQL, Redis, Weaviate). The docker-compose file handles all of this. Accessible at http://localhost/install after startup.

For production, add a reverse proxy and configure persistent storage volumes.

Limitations

• More complex deployment than Flowise or LangFlow (5+ containers)
• Heavier resource requirements (minimum 4GB RAM, 8GB recommended)
• Some advanced workflow features have a learning curve
• Enterprise features require paid tier (self-hosted community edition is free)

Best for: Teams building production AI applications who want monitoring, knowledge bases, and API generation in one platform.

Flowise — Best for Rapid Prototyping

Flowise is the developer-friendly visual builder. It's lighter than Dify, easier to deploy, and excellent for rapidly prototyping AI workflows. In 2025, Flowise was acquired by Workday, giving it enterprise backing.

What Makes It Stand Out

Developer playground feel: Flowise gives developers maximum control without sacrificing the visual building experience. Complex agent workflows, multi-step reasoning, custom code nodes — all available through the drag-and-drop interface.

Component breadth: Flowise has an extensive library of components:

• 100+ LLM integrations
• All major vector databases (Pinecone, Chroma, Weaviate, Qdrant, etc.)
• Memory components (conversation buffer, summary, Redis-backed)
• Tool integrations (web search, calculators, API calls)
• Chain types (sequential, conversational, SQL, ReAct agents)

Marketplace: Share and import workflow templates from the community marketplace.

Embedded chatbot: Generate an embeddable chat widget from any flow — add AI to your website with a script tag.

API and SDK: REST API for every flow, JavaScript/Python SDKs for programmatic access.

Self-Hosting

# NPM
npm install -g flowise
npx flowise start

# Docker
docker run -d \
  -p 3000:3000 \
  -v ~/.flowise:/root/.flowise \
  flowiseai/flowise

Single container, minimal configuration. This is one of Flowise's strongest advantages over Dify — extremely simple to get running.

Post-Acquisition Considerations

Workday's acquisition of Flowise in 2025 raises questions about long-term open source commitment. The project remains MIT licensed and actively developed, but enterprise product decisions are now made by Workday. Monitor the repository for any licensing changes before building critical systems on it.

Limitations

• Limited monitoring/analytics (Dify is much better here)
• No built-in model management UI
• Knowledge base support is good but less integrated than Dify
• Post-acquisition trajectory uncertain for pure open source users

Best for: Developers who want a lightweight, easy-to-deploy visual workflow builder for AI agent and chatbot prototyping.

LangFlow — Best for LangChain Users

LangFlow is the visual interface for LangChain. If your team is comfortable with LangChain concepts and wants to build workflows visually, LangFlow maps LangChain components to visual nodes you can connect on a canvas.

What Makes It Stand Out

Full LangChain component access: Every LangChain component — chains, agents, memory types, tools, document loaders, vector stores — is available as a visual node. No compromise on what you can build vs. writing LangChain code.

Python code export: Export any LangFlow workflow as executable Python code. Start visually, export to code, refine programmatically.

LangSmith integration: Native integration with LangSmith for tracing, debugging, and monitoring LangChain applications (LangSmith is a managed service with a generous free tier).

Deployment options: Self-hosted Docker, Datastax Langflow (managed cloud), or run locally.

Agent support: Multi-agent workflows, ReAct agents, tool-using agents — all buildable through the visual interface.

Self-Hosting

# Python
pip install langflow
python -m langflow run

# Docker
docker run -p 7860:7860 langflowai/langflow

Simple single-container deployment. Add environment variables for API keys and database configuration.

Limitations

• Requires LangChain familiarity to use effectively — the abstraction layer shows
• Less monitoring/analytics than Dify
• Some components are overly complex due to LangChain's own complexity
• Documentation quality varies by component

Best for: Teams already using LangChain who want visual workflow building and the ability to export to code.

Side-by-Side: Building a Customer Support Bot

To illustrate the differences, here's how you'd build a basic RAG-powered customer support bot in each tool:

In Dify

1. Create Knowledge Base → Upload support docs → Configure embeddings
2. Create Chatbot application
3. Add "Knowledge Retrieval" node → connect to knowledge base
4. Add "LLM" node → configure system prompt with customer service persona
5. Configure chat variables and memory
6. Deploy → get embedded chat widget code

Time to deploy: 20-30 minutes, no code

In Flowise

1. Create new flow on canvas
2. Drag "PDF Loader" → "RecursiveCharacterTextSplitter" → "Chroma VectorStore"
3. Add "ChatOllama" LLM node → "ConversationalRetrievalQAChain"
4. Connect retriever from Chroma to chain
5. Save → get API endpoint

Time to deploy: 15-25 minutes, minimal code

In LangFlow

1. Create new flow
2. Add "DirectoryLoader" → "CharacterTextSplitter" → "Chroma" nodes
3. Add "ChatOpenAI" → "ConversationalRetrievalChain"
4. Connect components → add "Memory" → export API

Time to deploy: 20-30 minutes, some LangChain knowledge helpful

Resource Requirements

Setup	Minimum RAM	Recommended	Docker Images
Dify	4GB	8GB+	6-8 containers
Flowise	1GB	2-4GB	1 container
LangFlow	1GB	2-4GB	1 container

For resource-constrained environments (small VPS), Flowise or LangFlow are significantly lighter than Dify.

Cost Comparison

All three have self-hosted community editions that are free. Enterprise/cloud versions vary:

Product	Self-Hosted (Free)	Cloud Plan
Dify	Yes (community)	Starts at $59/month
Flowise	Yes	Custom (Workday enterprise)
LangFlow	Yes	Datastax managed, starts free

The self-hosted versions cover most use cases. Cloud plans add managed infrastructure, support, and enterprise features.

Which One Should You Choose?

Choose Dify if:

• You're building production AI applications
• You need monitoring, analytics, and usage visibility
• You want knowledge bases + workflows + API in one tool
• Your team includes non-technical members who will build workflows

Choose Flowise if:

• You want the fastest path to a working prototype
• You're fine with a lighter-weight deployment
• You're building primarily for developers, not end users
• You accept the post-acquisition open source risk

Choose LangFlow if:

• You're already using LangChain in production
• You want to export workflows to Python code
• You need visual debugging with LangSmith integration
• Your team thinks in LangChain concepts

Find Your Workflow Builder

Browse all AI workflow tools on OSSAlt — compare Dify, Flowise, LangFlow, n8n AI, and every other major open source AI workflow platform with deployment guides and use case comparisons.